Aesthetics & Scales with Pokémon

pokemon <- read_csv("../data/pokemon.csv")

# take a look at the data:
pokemon |> 
  head() |> 
  kable()

pokemon |>
  ggplot() +
  geom_point(aes(x = defense, y = hp))

pokemon |>
  ggplot() +
  geom_point(aes(x = defense, y = hp)) +
  # reverse the y-axis
  scale_y_reverse()

pokemon |>
  ggplot() +
  geom_point(aes(x = defense, y = hp)) +
  scale_y_reverse() +
  # new:
  geom_text(aes(x = defense, y = hp, label = name))

pokemon |>
  ggplot() +
  geom_point(aes(x = defense, y = hp)) +
  scale_y_reverse() +
  # repel the text labels:
  geom_text_repel(aes(x = defense, y = hp, label = name)) +
  # limit the x-axis to `defense` of 150 or more:
  # `NA` ("Not Available") is a missing value indicator.
  # We use it here to say that there is no upper limit on the x-axis.
  scale_x_continuous(limits = c(150, NA))

pokemon |>
  ggplot() +
  geom_point(aes(x = defense, y = hp)) +
  scale_y_reverse() +
  geom_text_repel(aes(x = defense, y = hp, label = name)) +
  # make it easier to identify the precise values of `defense`:
  scale_x_continuous(limits = c(150, NA), n.breaks = 30)

pokemon |>
  filter(generation == 1) |>
  ggplot() +
  geom_point(aes(x = defense, y = hp, color = type_1)) +
  geom_text_repel(aes(x = defense, y = hp, label = name))

pokemon |>
  filter(generation == 1) |>
  filter(type_1 %in% c("Water", "Fire", "Grass")) |>
  ggplot() +
  geom_point(aes(x = defense, y = hp, color = type_1)) +
  geom_text_repel(aes(x = defense, y = hp, label = name)) +
  # use the `type_1` colors instead of the default:
  scale_color_manual(values = c(
    Water = "blue",
    Fire = "red",
    Grass = "green"
  ))

pokemon |>
  filter(generation == 1) |>
  ggplot() +
  # color the points by `stat_total` instead of `type1`:
  geom_point(aes(x = defense, y = hp, color = stat_total)) +
  # use the `viridis` color palette instead of the default:
  scale_color_viridis_c() +
  geom_text_repel(aes(x = defense, y = hp, label = name))

pokemon |>
  filter(generation == 1) |>
  # just water pokemon
  filter(type_1 == "Water") |>
  ggplot() +
  # new: `size` by `stat_total`
  geom_point(aes(x = defense, y = hp, size = stat_total)) +
  geom_text_repel(aes(x = defense, y = hp, label = name))

pokemon |>
  filter(generation == 1) |>
  # just psychic pokemon
  filter(type_1 == "Psychic") |>
  ggplot() +
  # new: `color` by `stat_total`, too
  geom_point(aes(x = defense, y = hp, size = stat_total, color = stat_total)) +
  # use the `viridis` color palette instead of the default:
  scale_color_viridis_c() +
  geom_text_repel(aes(x = defense, y = hp, label = name))

pokemon |>
  # filter for first gen
  filter(generation == 1) |>
  # filter for a few types
  filter(type_1 %in% c("Normal", "Rock", "Bug", "Poison")) |>
  ggplot() +
  geom_point(aes(
    x = defense,
    y = hp,
    # new: shape points by `type_1`
    shape = type_1,
    # color points by `stat_total`
    color = stat_total
  )) +
  scale_color_viridis_c() +
  geom_text_repel(aes(x = defense, y = hp, label = name))

# faceting allows us to split a plot into multiple panels based on a factor
pokemon |>
  filter(generation == 1) |>
  filter(type_1 %in% c("Normal", "Rock", "Bug", "Poison")) |>
  ggplot() +
  geom_point(aes(x = defense, y = hp, color = stat_total)) +
  scale_color_viridis_c() +
  # new: `~` means "by", so we are saying "facet wrap by `type_1`"
  facet_wrap(~type_1) +
  # note that the scales of the plots are all the same
  # this makes them directly comparable
  geom_text_repel(aes(x = defense, y = hp, label = name))

pokemon |>
  # we are "pivoting" the data from wide to long format for ease of plotting
  # we will discuss this later in the course
  pivot_longer(
    cols = c(attack, sp_attack, defense, sp_defense, speed),
    names_to = "stat_category",
    values_to = "stat_value"
  ) |>
  ggplot() +
  geom_point(aes(x = stat_value, y = hp, color = stat_total)) +
  scale_color_viridis_c() +
  # new: facet by `stat_category`, a column we created with `pivot_`
  facet_wrap(~stat_category)